Improved Penalty Strategies in Linear Regression Models

Autor:	Bahadır Yüzbaşı, S. Ejaz Ahmed, Mehmet Güngör
Jazyk:	angličtina
Rok vydání:	2017
Předmět:	sub-model Full Model Pretest and Shrinkage Estimation Multicollinearity Asymptotic and Simulation Statistics HA1-4737 Probabilities. Mathematical statistics QA273-280
Zdroj:	Revstat Statistical Journal, Vol 15, Iss 2 (2017)
Druh dokumentu:	article
ISSN:	1645-6726 2183-0371
DOI:	10.57805/revstat.v15i2.212
Popis:	We suggest pretest and shrinkage ridge estimation strategies for linear regression models. We investigate the asymptotic properties of suggested estimators. Further, a Monte Carlo simulation study is conducted to assess the relative performance of the listed estimators. Also, we numerically compare their performance with Lasso, adaptive Lasso and SCAD strategies. Finally, a real data example is presented to illustrate the usefulness of the suggested methods.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/624870a2613841a0b106c8ea699507ca Zobrazit plný text záznamu View record in DOAJ